The breakneck pace of digital transformation spurred by COVID-19 has continued to reshape the life sciences landscape in 2023. Rather than being defined by a single watershed moment, this year marked a turning point, as companies begun to move from a reactionary adoption to the deliberate integration of advanced technologies across the value chain. Over the year, the focus for life science companies once again remained on advancing R&D and on process efficiency and improving data quality, to become more data-driven, all whilst fighting to keep heads above water amidst increasingly diverse regulatory requirements and the need to drive greater productivity with fewer resources.
The overnight digital transformation sparked by the pandemic, now at the height of foundational disruption and optimisation, has cleared the path for the life sciences leaders to reach the next level in 2024, where data and technology amplify human potential rather than displace it. Let’s reflect on the key events that defined this transitional year, and glance at the future digital strategies that will be leading the shift from surviving to thriving.
Flirting with Data and Digital Transformation
Throughout 2023, the life sciences industry has been heavily focused on leveraging advanced technologies like Artificial Intelligence, Machine Learning, and Automation to drive innovation and accelerate research and development. Even though digital technologies present considerable opportunities for life sciences companies, most have yet to fully embrace and integrate these innovations in an ongoing, committed way that capitalises on their transformative potential.
Several organisations have been applying these tools and technologies to challenges like R&D, drug discovery, personalised medicine, and enhancing clinical trials. However, the industry is grappling with low[1]quality, outdated, and incomplete data, that is hindering the progress towards newer systems that rely precisely on this data.
In response to a greater understanding of data’s critical role in innovation and the need for a reduced time to market for new products, a wave of data-centricity in life science R&D processes opened up many new challenges for organisations; and particularly in regard to master data management, data governance, and data interoperability. The challenges have manifested not only in understanding who owns what data, but how the data items link together, how to track and trace this data, and how to perform impact analysis on changes to that data.
Some life science companies have made good strides this year in tapping into data’s potential to accelerate discoveries and outcomes; organisations are becoming more aligned with common definitions (defining Single Consistent Dose Strength in organisations that are IDMP ready, for example), and there has been an increase in the adoption of cloud-based systems and platforms to consolidate, analyse, and share data. But these organisations are battling with data gaps, cross-business data ownership, and a standard of data quality that is, in some cases, terrifyingly inconsistent. Is there the will – or the financial backing – to address this?
